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<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.superpoint.superpoint</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
from silk.backbones.silk.coords import (
    mapping_from_torch_module,
    CoordinateMappingProvider,
)
from silk.backbones.superpoint.magicpoint import MagicPoint, vgg_block
from silk.flow import AutoForward, Flow
from torchvision.transforms.functional import resize, InterpolationMode


class DescriptorHead(torch.nn.Module, CoordinateMappingProvider):
    def __init__(
        self,
        in_channels: int = 128,
        out_channels: int = 256,
        use_batchnorm: bool = True,
        padding: int = 1,
    ) -&gt; None:
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        # descriptor head (decoder)
        self._desH1 = vgg_block(
            in_channels,
            out_channels,
            3,
            use_batchnorm=use_batchnorm,
            padding=padding,
        )

        if use_batchnorm:
            # no relu (bc last layer) - option to have batchnorm or not
            self._desH2 = nn.Sequential(
                nn.Conv2d(out_channels, out_channels, 1, padding=0),
                nn.BatchNorm2d(out_channels),
            )
        else:
            # if no batch norm - note that normailzation is calculated later
            self._desH2 = nn.Sequential(
                nn.Conv2d(out_channels, out_channels, 1, padding=0),
            )

    def mappings(self):
        mapping = mapping_from_torch_module(self._desH1)
        mapping = mapping + mapping_from_torch_module(self._desH2)
        return mapping

    def forward(self, x: torch.Tensor):
        x = self._desH1(x)
        x = self._desH2(x)
        return x


class SuperPoint(AutoForward, torch.nn.Module):
    &#34;&#34;&#34;
    The SuperPoint model, as a subclass of the MagicPoint model.
    &#34;&#34;&#34;

    def __init__(
        self,
        *,
        use_batchnorm: bool = True,
        descriptor_scale_factor: float = 1.0,
        input_name: str = &#34;images&#34;,
        descriptor_head=None,
        descriptor_head_output_name=&#34;raw_descriptors&#34;,
        default_outputs=(&#34;coarse_descriptors&#34;, &#34;logits&#34;),
        **magicpoint_kwargs,
    ):
        &#34;&#34;&#34;Initialize the SuperPoint model.

        Assumes an RGB image with 1 color channel (grayscale image).

        Parameters
        ----------
        use_batchnorm : bool, optional
            Specify if the model uses batch normalization, by default True
        &#34;&#34;&#34;
        torch.nn.Module.__init__(self)

        self._descriptor_scale_factor = descriptor_scale_factor
        self.magicpoint = MagicPoint(
            use_batchnorm=use_batchnorm,
            input_name=input_name,
            **magicpoint_kwargs,
        )

        AutoForward.__init__(self, self.magicpoint.flow, default_outputs)

        self.magicpoint.backbone.add_head(
            descriptor_head_output_name,
            DescriptorHead(
                in_channels=128, out_channels=256, use_batchnorm=use_batchnorm
            )
            if descriptor_head is None
            else descriptor_head,
        )

        SuperPoint.add_descriptor_head_post_processing(
            self.flow,
            input_name=input_name,
            descriptor_head_output_name=descriptor_head_output_name,
            prefix=&#34;&#34;,
            scale_factor=self._descriptor_scale_factor,
        )

    @staticmethod
    def add_descriptor_head_post_processing(
        flow: Flow,
        input_name: str = &#34;images&#34;,
        descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
        prefix: str = &#34;superpoint.&#34;,
        scale_factor: float = 1.0,
    ):
        flow.define_transition(
            f&#34;{prefix}coarse_descriptors&#34;,
            partial(SuperPoint.normalize_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
        )
        flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
        flow.define_transition(
            f&#34;{prefix}sparse_descriptors&#34;,
            partial(SuperPoint.sparsify_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
            f&#34;{prefix}positions&#34;,
            f&#34;{prefix}image_size&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}upsampled_descriptors&#34;,
            partial(SuperPoint.upsample_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
            f&#34;{prefix}image_size&#34;,
        )

    @staticmethod
    def image_size(images):
        return images.shape[-2:]

    @staticmethod
    def normalize_descriptors(raw_descriptors, scale_factor=1.0, normalize=True):
        if normalize:
            return scale_factor * F.normalize(
                raw_descriptors,
                p=2,
                dim=1,
            )  # L2 normalization
        return scale_factor * raw_descriptors

    @staticmethod
    def sparsify_descriptors(
        raw_descriptors,
        positions,
        image_size,
        scale_factor: float = 1.0,
    ):
        image_size = torch.tensor(
            image_size,
            dtype=torch.float,
            device=raw_descriptors.device,
        )
        sparse_descriptors = []

        for i, pos in enumerate(positions):
            pos = pos[:, :2]
            n = pos.shape[0]

            # handle edge case when no points has been detected
            if n == 0:
                desc = raw_descriptors[i]
                fdim = desc.shape[0]
                sparse_descriptors.append(
                    torch.zeros(
                        (n, fdim),
                        dtype=desc.dtype,
                        device=desc.device,
                    )
                )
                continue

            # revert pixel centering for grad sample
            pos = pos - 0.5

            # normalize to [-1. +1] &amp; prepare for grid sample
            pos = 2.0 * (pos / (image_size - 1)) - 1.0
            pos = pos[:, [1, 0]]
            pos = pos[None, None, ...]

            # process descriptor output by interpolating into descriptor map using 2D point locations\
            # note that grid_sample takes coordinates in x, y order (col, then row)
            descriptors = raw_descriptors[i][None, ...]
            descriptors = F.grid_sample(
                descriptors,
                pos,
                mode=&#34;bilinear&#34;,
                align_corners=False,
            )
            descriptors = descriptors.view(-1, n).T

            # L2 normalize the descriptors
            descriptors = SuperPoint.normalize_descriptors(descriptors, scale_factor)

            sparse_descriptors.append(descriptors)
        return sparse_descriptors

    @staticmethod
    def upsample_descriptors(raw_descriptors, image_size, scale_factor: float = 1.0):
        upsampled_descriptors = resize(
            raw_descriptors,
            image_size,
            interpolation=InterpolationMode.BILINEAR,
        )
        return SuperPoint.normalize_descriptors(upsampled_descriptors, scale_factor)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.superpoint.superpoint.DescriptorHead"><code class="flex name class">
<span>class <span class="ident">DescriptorHead</span></span>
<span>(</span><span>in_channels: int = 128, out_channels: int = 256, use_batchnorm: bool = True, padding: int = 1)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class DescriptorHead(torch.nn.Module, CoordinateMappingProvider):
    def __init__(
        self,
        in_channels: int = 128,
        out_channels: int = 256,
        use_batchnorm: bool = True,
        padding: int = 1,
    ) -&gt; None:
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        # descriptor head (decoder)
        self._desH1 = vgg_block(
            in_channels,
            out_channels,
            3,
            use_batchnorm=use_batchnorm,
            padding=padding,
        )

        if use_batchnorm:
            # no relu (bc last layer) - option to have batchnorm or not
            self._desH2 = nn.Sequential(
                nn.Conv2d(out_channels, out_channels, 1, padding=0),
                nn.BatchNorm2d(out_channels),
            )
        else:
            # if no batch norm - note that normailzation is calculated later
            self._desH2 = nn.Sequential(
                nn.Conv2d(out_channels, out_channels, 1, padding=0),
            )

    def mappings(self):
        mapping = mapping_from_torch_module(self._desH1)
        mapping = mapping + mapping_from_torch_module(self._desH2)
        return mapping

    def forward(self, x: torch.Tensor):
        x = self._desH1(x)
        x = self._desH2(x)
        return x</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.superpoint.DescriptorHead.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.superpoint.DescriptorHead.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.superpoint.DescriptorHead.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x: torch.Tensor) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x: torch.Tensor):
    x = self._desH1(x)
    x = self._desH2(x)
    return x</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.superpoint.DescriptorHead.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = mapping_from_torch_module(self._desH1)
    mapping = mapping + mapping_from_torch_module(self._desH2)
    return mapping</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint"><code class="flex name class">
<span>class <span class="ident">SuperPoint</span></span>
<span>(</span><span>*, use_batchnorm: bool = True, descriptor_scale_factor: float = 1.0, input_name: str = 'images', descriptor_head=None, descriptor_head_output_name='raw_descriptors', default_outputs=('coarse_descriptors', 'logits'), **magicpoint_kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>The SuperPoint model, as a subclass of the MagicPoint model.</p>
<p>Initialize the SuperPoint model.</p>
<p>Assumes an RGB image with 1 color channel (grayscale image).</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>use_batchnorm</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>Specify if the model uses batch normalization, by default True</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SuperPoint(AutoForward, torch.nn.Module):
    &#34;&#34;&#34;
    The SuperPoint model, as a subclass of the MagicPoint model.
    &#34;&#34;&#34;

    def __init__(
        self,
        *,
        use_batchnorm: bool = True,
        descriptor_scale_factor: float = 1.0,
        input_name: str = &#34;images&#34;,
        descriptor_head=None,
        descriptor_head_output_name=&#34;raw_descriptors&#34;,
        default_outputs=(&#34;coarse_descriptors&#34;, &#34;logits&#34;),
        **magicpoint_kwargs,
    ):
        &#34;&#34;&#34;Initialize the SuperPoint model.

        Assumes an RGB image with 1 color channel (grayscale image).

        Parameters
        ----------
        use_batchnorm : bool, optional
            Specify if the model uses batch normalization, by default True
        &#34;&#34;&#34;
        torch.nn.Module.__init__(self)

        self._descriptor_scale_factor = descriptor_scale_factor
        self.magicpoint = MagicPoint(
            use_batchnorm=use_batchnorm,
            input_name=input_name,
            **magicpoint_kwargs,
        )

        AutoForward.__init__(self, self.magicpoint.flow, default_outputs)

        self.magicpoint.backbone.add_head(
            descriptor_head_output_name,
            DescriptorHead(
                in_channels=128, out_channels=256, use_batchnorm=use_batchnorm
            )
            if descriptor_head is None
            else descriptor_head,
        )

        SuperPoint.add_descriptor_head_post_processing(
            self.flow,
            input_name=input_name,
            descriptor_head_output_name=descriptor_head_output_name,
            prefix=&#34;&#34;,
            scale_factor=self._descriptor_scale_factor,
        )

    @staticmethod
    def add_descriptor_head_post_processing(
        flow: Flow,
        input_name: str = &#34;images&#34;,
        descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
        prefix: str = &#34;superpoint.&#34;,
        scale_factor: float = 1.0,
    ):
        flow.define_transition(
            f&#34;{prefix}coarse_descriptors&#34;,
            partial(SuperPoint.normalize_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
        )
        flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
        flow.define_transition(
            f&#34;{prefix}sparse_descriptors&#34;,
            partial(SuperPoint.sparsify_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
            f&#34;{prefix}positions&#34;,
            f&#34;{prefix}image_size&#34;,
        )
        flow.define_transition(
            f&#34;{prefix}upsampled_descriptors&#34;,
            partial(SuperPoint.upsample_descriptors, scale_factor=scale_factor),
            descriptor_head_output_name,
            f&#34;{prefix}image_size&#34;,
        )

    @staticmethod
    def image_size(images):
        return images.shape[-2:]

    @staticmethod
    def normalize_descriptors(raw_descriptors, scale_factor=1.0, normalize=True):
        if normalize:
            return scale_factor * F.normalize(
                raw_descriptors,
                p=2,
                dim=1,
            )  # L2 normalization
        return scale_factor * raw_descriptors

    @staticmethod
    def sparsify_descriptors(
        raw_descriptors,
        positions,
        image_size,
        scale_factor: float = 1.0,
    ):
        image_size = torch.tensor(
            image_size,
            dtype=torch.float,
            device=raw_descriptors.device,
        )
        sparse_descriptors = []

        for i, pos in enumerate(positions):
            pos = pos[:, :2]
            n = pos.shape[0]

            # handle edge case when no points has been detected
            if n == 0:
                desc = raw_descriptors[i]
                fdim = desc.shape[0]
                sparse_descriptors.append(
                    torch.zeros(
                        (n, fdim),
                        dtype=desc.dtype,
                        device=desc.device,
                    )
                )
                continue

            # revert pixel centering for grad sample
            pos = pos - 0.5

            # normalize to [-1. +1] &amp; prepare for grid sample
            pos = 2.0 * (pos / (image_size - 1)) - 1.0
            pos = pos[:, [1, 0]]
            pos = pos[None, None, ...]

            # process descriptor output by interpolating into descriptor map using 2D point locations\
            # note that grid_sample takes coordinates in x, y order (col, then row)
            descriptors = raw_descriptors[i][None, ...]
            descriptors = F.grid_sample(
                descriptors,
                pos,
                mode=&#34;bilinear&#34;,
                align_corners=False,
            )
            descriptors = descriptors.view(-1, n).T

            # L2 normalize the descriptors
            descriptors = SuperPoint.normalize_descriptors(descriptors, scale_factor)

            sparse_descriptors.append(descriptors)
        return sparse_descriptors

    @staticmethod
    def upsample_descriptors(raw_descriptors, image_size, scale_factor: float = 1.0):
        upsampled_descriptors = resize(
            raw_descriptors,
            image_size,
            interpolation=InterpolationMode.BILINEAR,
        )
        return SuperPoint.normalize_descriptors(upsampled_descriptors, scale_factor)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.flow.AutoForward" href="../../flow.html#silk.flow.AutoForward">AutoForward</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Static methods</h3>
<dl>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.add_descriptor_head_post_processing"><code class="name flex">
<span>def <span class="ident">add_descriptor_head_post_processing</span></span>(<span>flow: <a title="silk.flow.Flow" href="../../flow.html#silk.flow.Flow">Flow</a>, input_name: str = 'images', descriptor_head_output_name: str = 'raw_descriptors', prefix: str = 'superpoint.', scale_factor: float = 1.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def add_descriptor_head_post_processing(
    flow: Flow,
    input_name: str = &#34;images&#34;,
    descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
    prefix: str = &#34;superpoint.&#34;,
    scale_factor: float = 1.0,
):
    flow.define_transition(
        f&#34;{prefix}coarse_descriptors&#34;,
        partial(SuperPoint.normalize_descriptors, scale_factor=scale_factor),
        descriptor_head_output_name,
    )
    flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
    flow.define_transition(
        f&#34;{prefix}sparse_descriptors&#34;,
        partial(SuperPoint.sparsify_descriptors, scale_factor=scale_factor),
        descriptor_head_output_name,
        f&#34;{prefix}positions&#34;,
        f&#34;{prefix}image_size&#34;,
    )
    flow.define_transition(
        f&#34;{prefix}upsampled_descriptors&#34;,
        partial(SuperPoint.upsample_descriptors, scale_factor=scale_factor),
        descriptor_head_output_name,
        f&#34;{prefix}image_size&#34;,
    )</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.image_size"><code class="name flex">
<span>def <span class="ident">image_size</span></span>(<span>images)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def image_size(images):
    return images.shape[-2:]</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.normalize_descriptors"><code class="name flex">
<span>def <span class="ident">normalize_descriptors</span></span>(<span>raw_descriptors, scale_factor=1.0, normalize=True)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def normalize_descriptors(raw_descriptors, scale_factor=1.0, normalize=True):
    if normalize:
        return scale_factor * F.normalize(
            raw_descriptors,
            p=2,
            dim=1,
        )  # L2 normalization
    return scale_factor * raw_descriptors</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.sparsify_descriptors"><code class="name flex">
<span>def <span class="ident">sparsify_descriptors</span></span>(<span>raw_descriptors, positions, image_size, scale_factor: float = 1.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def sparsify_descriptors(
    raw_descriptors,
    positions,
    image_size,
    scale_factor: float = 1.0,
):
    image_size = torch.tensor(
        image_size,
        dtype=torch.float,
        device=raw_descriptors.device,
    )
    sparse_descriptors = []

    for i, pos in enumerate(positions):
        pos = pos[:, :2]
        n = pos.shape[0]

        # handle edge case when no points has been detected
        if n == 0:
            desc = raw_descriptors[i]
            fdim = desc.shape[0]
            sparse_descriptors.append(
                torch.zeros(
                    (n, fdim),
                    dtype=desc.dtype,
                    device=desc.device,
                )
            )
            continue

        # revert pixel centering for grad sample
        pos = pos - 0.5

        # normalize to [-1. +1] &amp; prepare for grid sample
        pos = 2.0 * (pos / (image_size - 1)) - 1.0
        pos = pos[:, [1, 0]]
        pos = pos[None, None, ...]

        # process descriptor output by interpolating into descriptor map using 2D point locations\
        # note that grid_sample takes coordinates in x, y order (col, then row)
        descriptors = raw_descriptors[i][None, ...]
        descriptors = F.grid_sample(
            descriptors,
            pos,
            mode=&#34;bilinear&#34;,
            align_corners=False,
        )
        descriptors = descriptors.view(-1, n).T

        # L2 normalize the descriptors
        descriptors = SuperPoint.normalize_descriptors(descriptors, scale_factor)

        sparse_descriptors.append(descriptors)
    return sparse_descriptors</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.upsample_descriptors"><code class="name flex">
<span>def <span class="ident">upsample_descriptors</span></span>(<span>raw_descriptors, image_size, scale_factor: float = 1.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def upsample_descriptors(raw_descriptors, image_size, scale_factor: float = 1.0):
    upsampled_descriptors = resize(
        raw_descriptors,
        image_size,
        interpolation=InterpolationMode.BILINEAR,
    )
    return SuperPoint.normalize_descriptors(upsampled_descriptors, scale_factor)</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.superpoint.SuperPoint.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, *args, **kwargs) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, *args, **kwargs):
    if self._forward_flow is None:
        self._forward_flow = self._flow.with_outputs(self._default_outputs)
    return self._forward_flow(*args, **kwargs)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.superpoint" href="index.html">silk.backbones.superpoint</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.superpoint.superpoint.DescriptorHead" href="#silk.backbones.superpoint.superpoint.DescriptorHead">DescriptorHead</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.superpoint.DescriptorHead.dump_patches" href="#silk.backbones.superpoint.superpoint.DescriptorHead.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.DescriptorHead.forward" href="#silk.backbones.superpoint.superpoint.DescriptorHead.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.DescriptorHead.mappings" href="#silk.backbones.superpoint.superpoint.DescriptorHead.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.DescriptorHead.training" href="#silk.backbones.superpoint.superpoint.DescriptorHead.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.superpoint.superpoint.SuperPoint" href="#silk.backbones.superpoint.superpoint.SuperPoint">SuperPoint</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.add_descriptor_head_post_processing" href="#silk.backbones.superpoint.superpoint.SuperPoint.add_descriptor_head_post_processing">add_descriptor_head_post_processing</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.dump_patches" href="#silk.backbones.superpoint.superpoint.SuperPoint.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.forward" href="#silk.backbones.superpoint.superpoint.SuperPoint.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.image_size" href="#silk.backbones.superpoint.superpoint.SuperPoint.image_size">image_size</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.normalize_descriptors" href="#silk.backbones.superpoint.superpoint.SuperPoint.normalize_descriptors">normalize_descriptors</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.sparsify_descriptors" href="#silk.backbones.superpoint.superpoint.SuperPoint.sparsify_descriptors">sparsify_descriptors</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.training" href="#silk.backbones.superpoint.superpoint.SuperPoint.training">training</a></code></li>
<li><code><a title="silk.backbones.superpoint.superpoint.SuperPoint.upsample_descriptors" href="#silk.backbones.superpoint.superpoint.SuperPoint.upsample_descriptors">upsample_descriptors</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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